Dual-View Trend Chart (Guest Post from Kasia Gasiewska-Holc)

We’re once again thrilled to have a guest blog post from our regular contributor, Kasia Gasiewska-Holc. Based in Poland, Kasia works remotely as a Senior BI Analyst at Ecovadis, a data analytics company based in Warsaw. She is also a Tableau Public Ambassador and she loves using Tableau as a creative outlet for data viz experimentations.

 

In this tutorial, we’re going to learn how to build an enhanced, dual-view trend chart. This chart allows you to focus on a specific date range while still retaining the broader context of how the selected timeframe compares to the entire dataset. Here’s an example of one of my latest projects, showcasing this cool data viz solution:

 

 

You can access the interactive version of the viz here: Dual-View Trend Chart

 

Why Use an Enhanced Dual-View Trend Chart?

Traditional trend charts often force data visualization designers to make a compromise: either show the full date range, which risks cluttering the view and making it illegible, or limit the number of data points displayed by default, which forces the end user to manually adjust the view, making it difficult to compare the selected timeframe to the whole dataset.

 

The dual-view chart addresses that challenge by offering both a detailed view of a specific date range and an overview of the entire data context.

 

Step-by-Step Tutorial

 

Step 1: Create the Main Chart

 

We will begin by creating our main chart. It will include all dates in the dataset, but we will use a table calculation to control which marks are shown or hidden. The idea is to consistently display the same number of marks in the view, giving the illusion of scrolling through the chart by using a date range slider. For this tutorial, we’ll use the Orders table from the Sample Superstore dataset, which you can download here.

 

Start by connecting to the data, navigating to a new worksheet and dragging Order Date and Sales to rows. Set Order Date to a continuous weekly granularity and Sales to the sum aggregation function.

 

 

This will be the foundation of our weekly view.

 

Step 2: Create a Parameter to Control the Date Range in the View

 

Next, let’s create a parameter that will serve as a scrollbar. The parameter will return a value that we’ll use in a table calculation to control which marks are shown or hidden on the chart. First, we need to number every data point on the chart so that we can later compare it to the parameter. For this, we’ll use an Index calculation computed across the table. I also decided to display the Index calculation on labels, so that it’s easy to see the number of marks currently included in the view.

 

 

Important Note: If your data has gaps for certain date values (e.g., some weeks have no data), be sure to click on the Date pill in the Columns shelf and select ‘Show Missing Values’. This step is crucial for ensuring that the Index calculation is accurately computed across the table. Compare the Index values in the two screenshots below, which show Sales by day. In the left screenshot, missing values are not displayed, causing the Index calculation to ignore missing dates. In the right screenshot, missing values are shown, and the Index field accounts for these gaps. Displaying missing values is essential for this chart, as it allows for smooth scrolling through the view without abrupt jumps when a data point is missing.

 

 

Now, we can create the ‘scroll’ parameter. The parameter value will be compared to our data point Index, but to display more than one point in the main view, we’ll need to display all points that have an Index value within the range of: [Parameter Value] to [Parameter Value] + [Number of Points to Display - 1]. In our example, we will aim to display 50 weeks at a time so our range would always be defined as: from [Parameter Value] to [Parameter Value] + 49. Here are our parameter settings:

 

 

You’ll notice that the range of values is sourced from a separate calculation, called Date parameter range:

 

 

The calculation allows us to retrieve a range of all data points in the view (from 0 to 159, which corresponds with the total number of weeks in the dataset). The DATEDIFF function is used to mimic the Index table calculation and we are subtracting 49 instead of 50 because at the first mark the DATEDIFF function will return 0 instead of 1. The max value represents the total number of data points (similar to the Index function but we can't pass the Index calculation directly to the parameter), minus the number of points we want to display in the view. Why do we need the subtraction? Because we always want to show a window of 50 marks in the view. Without it, users would be able to adjust the parameter so that less than 50 weeks are displayed.

 

The min/max range of parameter values is necessary to automatically adjust the slider range when new dates are added or some data points are removed. We could use fixed values instead, if we’re certain our date range won’t change and no filters will be applied that might cause certain dates to drop from the view.

 

Now, let’s create a Show/Hide Marks calculation that uses the parameter to control the visibility of marks on the chart:

 

 

Apply this calculation as a filter to only show True values. Change the view setting to fit ’Entire View’. Here’s the result after applying the filter and adjusting the parameter.

 

 

Notice what happens when mark #12 drops out of the view—the Y-axis range automatically adjusts to the maximum value in the current view. While this dynamic scaling might be acceptable in some cases, I find it more effective to keep the scale consistent across the entire chart, regardless of the current view’s maximum value. However, we don’t want to fix the axis as this would ignore any filters a user might apply. Instead, we’ll use one of my favorite tricks: adding an invisible reference line that forces the Y-axis to remain static, based on the all-time maximum weekly sales, ensuring the scale remains consistent.

 

First, let’s create a Reference line calculation that returns all-time maximum value:

 

 

Drag the calculation to the Detail card. Then, navigate to the Analytics pane and select ‘Reference Line’, using the following settings:

 

 

Now, look what happens when point #12 is dropped from the view. The Y-axis remains unchanged!

 

 

Step 3: Create the Mini Highlight Chart

Next, we’ll add the mini highlight chart. While our main chart shows only 50 marks at a time, the mini chart will display all marks, providing context for the general trend and seasonality across all dates. We’ll also add a highlight backdrop to indicate the location of the marks currently viewed in the main chart, relative to all dates.

 

To create the mini chart, duplicate the main chart and remove the existing Show/Hide Marks filter. Here, we don’t want to hide any dates from the view; instead, we’ll use our parameter value to display a reference band. Create a new Reference band calculation, using the following syntax:

 

 

Add the Reference band calculation to Rows as a continuous pill and combine this with the SUM(Sales) pill into a dual-axis view. Format the Reference band pill as an area chart.

 

 

You can now place both charts on a dashboard and display the parameter that will control both of them, using the ‘Slider’ option.

 

Step 4: Add Min. and Max. Value Spotlights to the Main Chart (Optional)

This step is optional but can add significant analytical value to the view. We can enhance the main chart by highlighting ‘local’ minimum and maximum values. By ‘local,’ I mean the minimum and maximum values specific to weeks currently displayed in the view, which will update as you slide through the chart.

 

To achieve this, navigate to the main chart and duplicate the SUM(Sales) pill on the rows. Create a dual-axis view from the two pills and change the new pill’s mark type to ‘circle’. Next, create a calculation that identifies whether a point is a local minimum, maximum, or neither. Here’s the syntax:

 

 

With [Sales in View] being:

 

 

Add this calculation to the color card and adjust the colors for Min/Max/Null values. For a Null value, I recommend using a transparent color (to learn how to do this, check out Kevin’s blog post, Introducing the Transparent Color Hex Code in Tableau).

 

You can also use the Min/Max/Null to display Sales value as a label. I opted out of this, but at this stage a wide range of formatting options is available.

 

Conclusion

And there you have it! While this tutorial may seem lengthy, the dual-view trend chart is fairly simple to implement and offers significant value for users needing to analyze both focused trends and broader patterns simultaneously. I hope you find this approach helpful for your next data visualization project!

 

Kasia Gasiewska-Holc

September 30, 2024